Method and system for a double search user group selection scheme with range in TDD multiuser MIMO downlink transmission

ABSTRACT

Certain aspects of a method and system for processing signals in a communication system may include maximizing system capacity for a time division duplex (TDD) multiple-input multiple-output (MIMO) system, based on reducing a search range within which to find a group of signals having maximum channel gain. At least one of: a first signal for a first user and a second signal for a second user may be selected, which are both within the reduced search range, and which provides a maximum system capacity. The first signal for the first user may be selected from the reduced search range corresponding to a channel gain that is greater than a channel gain corresponding to a remaining portion of the reduced search range. The reduced search range may be generated by sorting a plurality of signals based on a channel gain corresponding to each of the plurality of signals.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

This application makes reference to:

U.S. application Ser. No. 11/232,340 filed Sep. 21, 2005;

U.S. application Ser. No. 11/232/266 filed Sep. 21, 2005;

U.S. application Ser. No. 11/231,501 filed Sep. 21, 2005;

U.S. application Ser. No. 11/231,699 filed Sep. 21, 2005;

U.S. application Ser. No. 11/232,369 filed Sep. 21, 2005;

U.S. application Ser. No. 11/231,701 filed Sep. 21, 2005;

U.S. application Ser. No. 11/232,362 filed Sep. 21, 2005;

U.S. application Ser. No. 11/231,557 filed Sep. 21, 2005; and

U.S. application Ser. No. 11/231,416 filed Sep. 21, 2005.

Each of the above stated applications is hereby incorporated byreference in its entirety.

FIELD OF THE INVENTION

Certain embodiments of the invention relate to time division duplex(TDD) multiuser multiple-input multiple-output (MIMO) downlinktransmission. More specifically, certain embodiments of the inventionrelate to a method and system for a double search user group selectionscheme with range reduction in TDD multiuser MIMO downlink transmission.

BACKGROUND OF THE INVENTION

Mobile communications have changed the way people communicate and mobilephones have been transformed from a luxury item to an essential part ofevery day life. The use of mobile phones is today dictated by socialsituations, rather than hampered by location or technology. While voiceconnections fulfill the basic need to communicate, and mobile voiceconnections continue to filter even further into the fabric of every daylife, the mobile Internet is the next step in the mobile communicationrevolution. The mobile Internet is poised to become a common source ofeveryday information, and easy, versatile mobile access to this datawill be taken for granted.

Third generation (3G) cellular networks have been specifically designedto fulfill these future demands of the mobile Internet. As theseservices grow in popularity and usage, factors such as cost efficientoptimization of network capacity and quality of service (QoS) willbecome even more essential to cellular operators than it is today. Thesefactors may be achieved with careful network planning and operation,improvements in transmission methods, and advances in receivertechniques. To this end, carriers need technologies that will allow themto increase downlink throughput and, in turn, offer advanced QoScapabilities and speeds that rival those delivered by cable modem and/orDSL service providers.

In order to meet these demands, communication systems using multipleantennas at both the transmitter and the receiver have recently receivedincreased attention due to their promise of providing significantcapacity increase in a wireless fading environment. These multi-antennaconfigurations, also known as smart antenna techniques, may be utilizedto mitigate the negative effects of multipath and/or signal interferenceon signal reception. It is anticipated that smart antenna techniques maybe increasingly utilized both in connection with the deployment of basestation infrastructure and mobile subscriber units in cellular systemsto address the increasing capacity demands being placed on thosesystems. These demands arise, in part, from a shift underway fromcurrent voice-based services to next-generation wireless multimediaservices that provide voice, video, and data communication.

The utilization of multiple transmit and/or receive antennas is designedto introduce a diversity gain and to raise the degrees of freedom tosuppress interference generated within the signal reception process.Diversity gains improve system performance by increasing receivedsignal-to-noise ratio and stabilizing the transmission link. On theother hand, more degrees of freedom allow multiple simultaneoustransmissions by providing more robustness against signal interference,and/or by permitting greater frequency reuse for higher capacity. Incommunication systems that incorporate multi-antenna receivers, a set ofM receive antennas may be utilized to null the effect of (M−1)interferers, for example. Accordingly, N signals may be simultaneouslytransmitted in the same bandwidth using N transmit antennas, with thetransmitted signal then being separated into N respective signals by wayof a set of N antennas deployed at the receiver. Systems that utilizemultiple transmit and receive antennas may be referred to asmultiple-input multiple-output (MIMO) systems. One attractive aspect ofmulti-antenna systems, in particular MIMO systems, is the significantincrease in system capacity that may be achieved by utilizing thesetransmission configurations. For a fixed overall transmitted power, thecapacity offered by a MIMO configuration may scale with the increasedsignal-to-noise ratio (SNR). For example, in the case of fadingmultipath channels, a MIMO configuration may increase system capacity bynearly M additional bits/cycle for each 3-dB increase in SNR.

The widespread deployment of multi-antenna systems in wirelesscommunications has been limited by the increased cost that results fromincreased size, complexity, and power consumption. This poses problemsfor wireless system designs and applications. As a result, some initialwork on multiple antenna systems may be focused on systems that supportsingle user point-to-point links. However, the use of multi-antennatechniques for a multiuser environment to improve total throughputremains a challenge.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of such systems with some aspects of the present invention asset forth in the remainder of the present application with reference tothe drawings.

BRIEF SUMMARY OF THE INVENTION

A system and/or method is provided for a double search user groupselection scheme with range reduction in time division duplex (TDD)multiuser multiple-input multiple-output (MIMO) downlink transmission,substantially as shown in and/or described in connection with at leastone of the figures, as set forth more completely in the claims.

These and other advantages, aspects and novel features of the presentinvention, as well as details of an illustrated embodiment thereof, willbe more fully understood from the following description and drawings.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1A is a top-level block diagram illustrating an exemplary multiusermultiple-input-multiple-output (MIMO) downlink transmission system withlinear preceding, in accordance with an embodiment of the invention.

FIG. 1B illustrates a block diagram of a multiuser downlinkcommunication environment that may be utilized in connection with anembodiment of the invention.

FIG. 2A is a flowchart illustrating double search user group selectionscheme with range reduction, in accordance with an embodiment of theinvention.

FIG. 2B is a flow chart that illustrates exemplary steps in a method fora range reduction scheme for user selection in a multiuser MIMO downlinktransmission, in accordance with an embodiment of the invention.

FIG. 3 is a graph illustrating a comparison of sum capacity of a systemusing an optimal brute-force user selection with L=100, a double searchalgorithm with L=10 and a double search algorithm with L=5, inaccordance with an embodiment of the invention.

FIG. 4 is a graph illustrating a comparison of bit error rate (BER) of asystem using an optimal brute-force user selection with L=100, a doublesearch algorithm with L=10 and a double search algorithm with L=5, inaccordance with an embodiment of the invention.

FIG. 5 is a flowchart illustrating double search user group selectionscheme with range reduction in TDD multiuser MIMO downlink transmission,in accordance with an embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

Certain aspects of a method and system for processing signals in acommunication system may comprise maximizing system capacity based onreducing a search range within which to find a group of signals havingmaximum channel sum transmission rate, for a time division duplex (TDD)multiple-input multiple-output (MIMO) system. At least one of: a firstsignal for a first user and a second signal for a second user may beselected, which are both within the reduced search range, and whichprovides a maximum system capacity.

FIG. 1A is a top-level block diagram illustrating an exemplary multiusermultiple-input-multiple-output (MIMO) downlink transmission system withlinear precoding, in accordance with an embodiment of the invention.Referring to FIG. 1A, there is shown a communication system 100 that maycomprise a base station 102 a and a plurality of users 122 a, . . . ,130 a. In the communication system 100, the base station 102 a may beequipped with M antennas and K users 122 a, . . . , 130 a may each havea single antenna. In this implementation, the total number of users orreceiver antennas may be equal or higher than the number of base stationantennas, that is, K≧M.

The base station 102 a may comprise a plurality of channel encoders 104a, . . . , 106 a, a user scheduler 108 a, a plurality of modulators(MOD) 110 a, . . . , 112 a, a power control block 114 a, a beamformingor linear precoding block 116 a, an uplink channel estimator block 152,a processor 154, a memory 156 and a range reduction algorithm block 160.Each of the plurality of users 122 a, . . . , 130 a may comprise one ofa plurality of demodulators (DEM) 132 a, . . . , 140 a, and one of aplurality of channel decoders 142 a, . . . , 150 a.

The channel encoders 104 a, . . . , 106 a may comprise suitable logic,circuitry, and/or code that may be adapted to encode binary data foreach of the K users in the communication system 100. The beamforming orlinear precoding block 116 a may comprise suitable logic, circuitry,and/or code that may be adapted to processes the user data symbols toseparate signals intended for different users such that each userreceives little or no interference from other users. With M antennas atthe base station 102 a, the beamforming or linear precoding block 116 amay separate at most M different signals, that is, the base station 102a may transmit to at most M users at a time. Therefore, for each channelrealization, the base station 102 a may need to select M or less than Musers among all the K users to transmit.

The user scheduler 108 a may comprise suitable logic, circuitry, and/orcode that may be adapted to find a best user group that optimizescertain performance criterion such as the sum throughput of the system,for example. In this regard, the user scheduler 108 a may be adapted toperform the steps of a double search user selection algorithm to findthe best user group. The user scheduler 108 a may utilize knowledge ofthe channel state information (CSI) provided by the uplink channelestimator block 152 when determining the best user group. For a timedivision duplex (TDD) system, the base station 102 a may be adapted toestimate the uplink channel and use it as the downlink channel based onthe channel reciprocity property between the uplink and downlink. Thebase station 102 a may be assumed to have channel knowledge of everyuser through accurate uplink channel estimation.

The user scheduler 108 a may be adapted to select a first user with thestrongest channel gain and a second user with the next second strongestchannel gain. The user scheduler 108 a may be adapted to determine afirst maximum system capacity based on the first user and a secondmaximum system capacity based on the second user. The user scheduler 108a may also be adapted to select the highest of the first maximum systemcapacity and the second maximum system capacity as the maximum systemcapacity to be supported by the communication system 100. In thisregard, for a case when M=2, the user scheduler 108 a may select theuser group comprising a pair of users associated with the maximum systemcapacity selected.

The modulators 110 a, . . . , 112 a may comprise suitable logic,circuitry, and/or code that may be adapted to modulate the binary dataof each of the users selected by the user scheduler 108 a. In thisregard, the modulation operation on the binary data may result in aplurality of complex symbols, for example. The power control block 114 amay comprise suitable logic, circuitry, and/or code that may be adaptedto allocate different users with different power levels in accordancewith their respective channel quality, for example.

The user scheduler 108 a, the power control block 114 a, and/or thebeamforming or linear precoding block 116 may require knowledge of thestate of the downlink channel. The uplink channel estimator block 152may comprise suitable logic, circuitry, and/or code that may be adaptedto estimate, store and/or transfer channel state information associatedwith the users 122 a, . . . , 130 a. In this regard, the uplink channelestimator block 152 may be adapted to transfer the channel stateinformation to the user scheduler 108 a, the power control block 114 a,and/or the beamforming or linear preceding block 116 when necessary.

The processor 154 may comprise suitable logic, circuitry, and/or codethat may be adapted to process information and/or data associated withthe generation of transmission signals at the base station 102 a. Theprocessor 154 may also be adapted to control at least a portion of theoperations of the base station 102 a, for example, the processor 154 maybe adapted to maximize system capacity based on reducing a search rangewithin which to find a group of signals having maximum channel sumtransmission rate. The memory 156 may comprise suitable logic,circuitry, and/or code that may be adapted to store data and/or controlinformation that may be utilized in the operation of at least a portionof the base station 102 a.

The demodulators 132 a, . . . , 140 a in the users 122 a, . . . , 130 amay comprise suitable logic, circuitry, and/or code that may be adaptedto demodulate the signals received from the base station 102 a, forexample. The channel decoders 142 a, . . . , 150 a may comprise suitablelogic, circuitry, and/or code that may be adapted to decode thedemodulated signals from the demodulators 132 a, . . . , 140 a intobinary bit streams, for example.

The range reduction algorithm block 160 may comprise suitable logic,circuitry, and/or code that may be adapted to reduce the user searchrange from the plurality of users. A desired user group may bedetermined by searching among the L strongest users. The range reductionalgorithm may be assumed to be carried out offline at the system designstage. Notwithstanding, an embodiment of the invention may incorporatean adaptive algorithm to update L at real time with online channelmeasurements while using the offline calculated user range as theinitial value. Based on the reduction range [1:L], the search may berestricted within the first L strongest users for a user pair [idx₁,idx₂] that has the maximum instantaneous sum capacity.

FIG. 1B illustrates a block diagram of a finite rate multiusercommunication environment that may be utilized in connection with anembodiment of the invention. Referring to FIG. 1B, there is shown amultiuser downlink communication system with a base station 170, aplurality of transmit antennas, 152 _(1 . . . M), and a plurality ofreceive antennas, 154 _(1 . . . K), one antenna for each of the K users.

The base station 170 may comprise suitable logic, circuitry and/or codethat may be adapted to relay signals to and from mobile terminals orhandsets within a given range. The operation of the base station 170 maybe substantially similar to the operation of the base station 102 adescribed in FIG. 1A. The plurality of transmit antennas 172_(1 . . . M) may transmit processed RF signals to the plurality ofreceive antennas 174 _(1 . . . K). The plurality of receive antennas 174_(1 . . . K) may each receive a portion of the transmitted signal.

The signal model may be represented as

$\begin{matrix}{{\begin{bmatrix}y_{1} \\y_{2} \\\vdots \\y_{k}\end{bmatrix} = {{\begin{bmatrix}h_{1} \\h_{2} \\\vdots \\h_{k}\end{bmatrix}x} + n}},} & (1)\end{matrix}$where y_(k) (k=1, . . . , K) is the received signal by user k, h_(k)ε

^(×M) is the channel vector to user k, xε

^(M×1) is the transmitted symbol vector by the base station 170, and nε

^(K×1) is the additive white Gaussian noise (AWGN) with zero mean andunit variance. The transmitted symbols may satisfy a power constraint, Prepresented byE[x^(H)x]≦P,where (·)^(H) represents complex conjugate transpose.

Each element in h_(k) may be assumed to be a zero-mean circularlysymmetric complex Gaussian (ZMCSCG) random variable with unit variance.The users may be assumed to experience independent fading. The channelvectors {h_(k)}_(k=1) ^(K) may be statistically independent to eachother. The channel state information (CSI), h_(κ), may be assumed to beknown to user k, but not to other users. For a time division duplex(TDD) system, the base station may be adapted to estimate the uplinkchannel and use it as the downlink channel based on the channelreciprocity property between the uplink and downlink. The base station170 may be assumed to have channel knowledge of every user throughaccurate uplink channel estimation.

The zero-forcing (ZF) linear precoder may achieve a sum capacity whenthe number of users, K, approach infinity. The ZF precoders may beadapted to provide near-optimal performance even with a limited numberof users, for example, K=10 users. The zero-forcing precoders are aspecific type of linear precoders. When the base station 170 decides totransmit to a group of users D⊂{1, . . . , K} with d=|D|≦K, a linearpreceding scheme may linearly weigh the data symbols, s=[s₁, . . . ,s_(d)]^(T) before they are transmitted from the base station 170according to,x=FPs,  (2)where x is the transmitted signal vector as in (1), F=[f₁, . . . ,f_(d)] is the M×d linear preceding matrix with normalized columns(∥f_(k)∥=1), and P=diag{P₁, . . . , P_(d)} with

${\sum\limits_{i = 1}^{d}\; P_{i}} \leq P$is the power control matrix that allocates transmit power to differentusers. The data symbols s may correspond to the data symbols u₁ . . .u_(M) that are generated by the plurality of modulators 110 a . . . 112a. The elements in the linear preceding matrix F may represent theplurality of weighing coefficients utilized by the precoder 116 a. Thenonzero elements in the diagonal matrix P may represent the plurality ofscaling factors p₁ . . . p_(M) utilized by the power control block 114a. The received signal may be represented by the following equation:

$\begin{matrix}{\begin{bmatrix}y_{1} \\y_{2} \\\vdots \\y_{d}\end{bmatrix} = {{\begin{bmatrix}h_{1} \\h_{2} \\\vdots \\h_{d}\end{bmatrix}{FPs}} + {n.}}} & (3)\end{matrix}$

A zero-forcing precoder may utilize the pseudo-inverse of the overallchannel matrix H_(D)=[h₁ ^(T), . . . , h_(d) ^(T)]^(T) as the weightingmatrix when H_(D) has full row rank according to the following equation:

$\begin{matrix}{{W_{D} = {H_{D}^{\dagger} = {H_{D}^{H}\left( {H_{D}H_{D}^{H}} \right)}^{- 1}}},} & (4) \\{{F_{D} = {W_{D}\begin{bmatrix}\frac{1}{w_{1}} & \; & \; \\\; & ⋰ & \; \\\; & \; & \frac{1}{w_{d}}\end{bmatrix}}},} & (5)\end{matrix}$where {w_(i)}_(i=1) ^(d) are the columns of W_(D). By defining

$\begin{matrix}{\xi_{i}\overset{\Delta}{=}\frac{1}{w_{i}}} & (6)\end{matrix}$and substituting (5) in (3), the received signal y_(i) for each userwith zero-forcing preceding may be represented according to thefollowing expression:y _(i)=ξ_(i) P _(i) s _(i) +n _(i) ,∀iεD.  (7)

The multiuser downlink channel may be represented as a set of parallelchannels. The maximum system sum capacity of the given user group D,C_(D) may be represented as

$\begin{matrix}{{C_{D} = {\sum\limits_{i \in D}\;{\log\left( {1 + {\xi_{i}P_{i}}} \right)}}},} & (8)\end{matrix}$where the optimal P_(i) is given by a water-filling solution that may berepresented as,

$\begin{matrix}{{P_{i} = \left( {\mu - \frac{1}{\xi_{i}}} \right)^{+}},} & (9)\end{matrix}$with a water level μ chosen to satisfy

${\sum\limits_{i \in D}\;\left( {\mu - \frac{1}{\xi_{i}}} \right)^{+}} = {P.}$The maximum achievable sum capacity for a given channel realization, Cmay be obtained by searching over all the possible user groups accordingto

$\begin{matrix}{C = {\max\limits_{{D \subseteq {\{{1,\mspace{11mu}\ldots\mspace{11mu},K}\}}},{{D} \leq M}}{C_{D}.}}} & (10)\end{matrix}$

According to (10), for a given channel realization, the optimalbrute-force user group selection for ZF precoding requires searchingover all

$\sum\limits_{i = 1}^{M}\;\begin{pmatrix}K \\i\end{pmatrix}$possible user groups to find the user group with the maximum system sumcapacity. In addition, for each candidate user group, a water-fillingsolution needs to be computed to obtain the corresponding maximum systemsum capacity. As a result, a fairly high computational cost may beexpected, especially when K is large.

A double search user group selection scheme may be combined with searchrange reduction, where the L strongest users are selected. When numberof antennas at the base station 170, M=2, the number of candidate usergroups may be 2 L−1 as opposed to

$\frac{\kappa\left( {\kappa + 1} \right)}{2}$for the brute-force search algorithm. A significant reduction insearching complexity may be achieved, especially when L<<K.Third-generation cellular communication standards such as WCDMA and CDMA2000 typically employ two antennas at the base station 170.Notwithstanding, an embodiment of the invention may extend the algorithmto the case with any M number of antennas at the base station 170.

FIG. 2A is a flowchart illustrating double search user group selectionscheme with range reduction, in accordance with an embodiment of theinvention. Referring to FIG. 2A, exemplary steps may start at step 200.In step 202, the plurality of K users may be sorted according to theirchannel power. The CSI may be assumed to be available at the basestation 170 (FIG. 1B), in a sense that the multiple-input-single-output(MISO) channel impulse response h_(k)ε

^(1×2) (M=2) of each user is known at the transmitter. For each channelrealization, the users may be sorted and indexed in terms of theirchannel gains according to,γ₁≧γ₂≧ . . . ≧γ_(K).  (11)

In step 204, a range reduction algorithm may be applied to determine thereduced user search range L. A desired user group may be determined bysearching among the L strongest users. In accordance with an embodimentof the invention, the step 204 may be carried out offline at the systemdesign stage. Notwithstanding, an embodiment of the invention mayincorporate an adaptive algorithm to update L at real time with onlinechannel measurements while using the offline calculated user range asthe initial value. Based on the reduction range [1:L] obtained from step204, the search may be restricted within the first L strongest users fora user pair [idx₁, idx₂] that has the maximum instantaneous sumcapacity.

U.S. application Ser. No. 11/231,699 filed Sep. 21, 2005, provides adetailed description of a range reduction algorithm, and is herebyincorporated by reference in its entirety.

Utilizing the double search algorithm, the first candidate of the userpair may be restricted to be the first and second strongest users (user1 and user 2). In step 206, the maximum system capacity C_(max) may beinitialized to C_(max)=C(1), which corresponds to the case where basestation 170 only transmits to the strongest user according to thefollowing expression:C _(max) =C(1)=log₂(1+ρ·γ₁),  (12)where ρ is the average signal to noise ratio (SNR) of the system. Theoptimal user index may be initialized to be [idx₁, idx₂]=[1,0] withidx₂=0 representing that there is no second user. In step 208, theiteration variable k may be incremented according to k=i+1. In step 210,it may be determined whether C(i,k) is greater than the system maximumsum capacity C_(max), which may be given by the expression:

$\begin{matrix}{\begin{matrix}{{C\left( {i,k} \right)} = {{\log_{2}\left( {1 + {\frac{1}{2}{\rho \cdot \gamma_{i} \cdot \alpha_{i,k}}}} \right)} +}} \\{{\log_{2}\left( {1 + {\frac{1}{2}{\rho \cdot \gamma_{k} \cdot \alpha_{i,k}}}} \right)},}\end{matrix}{{i = 1},{{2\mspace{14mu} k} = 2},\ldots\mspace{11mu},L,}} & (13)\end{matrix}$where ρ is the average SNR of the system, and α_(i,k) is a parameterthat describes the orthogonality between h_(i) and h_(κ), according tothe following expression:

$\begin{matrix}{\alpha_{i,k} = {1 - {\frac{{\left\langle {h_{i},h_{k}} \right\rangle }^{2}}{{h_{i}}^{2} \cdot {h_{k}}^{2}}.}}} & (14)\end{matrix}$The possible user pairs [i,k] with i=1,2 and k=2, . . . , L may beexamined and compared to the system maximum sum capacity C_(max), withC(i,k).

If the system capacity C(i,k) is greater than the current maximumcapacity C_(max), control passes to step 212. In step 212, the maximumcapacity C_(max) may be updated with C(i,k) and the optimal user index[idx₁, idx₂] may be updated by [i,k]. Control then passes to step 214.If the system capacity C(i,k) is not greater than the current maximumcapacity C_(max), control passes to step 214.

In step 214, it may be determined whether the iteration variable k isless than or equal to the number of reduced strongest users L. If theiteration variable k is less than or equal to the number of reducedstrongest users L, control passes to step 216. In step 216, theiteration variable k may be incremented by 1 and control passes to step210. If the iteration variable k is not less than or equal to the numberof reduced strongest users L, control passes to step 218.

In step 218, it may be determined whether the iteration variable i isless than or equal to the number of transmit antennas M=2. If theiteration variable i is less than or equal to the number of transmitantennas M=2, control passes to step 220. In step 220 the iterationvariable i may be incremented by 1. Control then passes to step 208. Ifthe iteration variable i is not less than or equal to the number oftransmit antennas M=2, control passes to step 222.

In step 222, an optimal user pair index [idx₁, idx₂] may be determinedto calculate the maximum system sum capacity. If both idx₁ and idx₂ arevalid indices between 1 and K, then the base station 170 may be adaptedto communicate to both user idx₁ and user idx₂ at the same timeutilizing spatial multiplexing. The transmitter preceding matrix F maybe formed according to the following expression:F=[v _(idx) ₂ ^(⊥) v _(idx) ₁ ^(⊥)]*/√{square root over (2)}  (15)where vectors idx₁ and idx₂ are the unit norm directional vectors givenby the expression:

$\begin{matrix}{{v_{{idx}_{1}} = \frac{h_{{idx}_{1}}}{h_{{idx}_{1}}}},{v_{{idx}_{2}} = \frac{h_{{idx}_{2}}}{h_{{idx}_{2}}}},} & (16)\end{matrix}$

The precoder provided in equation (15) is equivalent to the form givenby (5). If idx₂ is equal to 0, the base station 170 may be communicatingonly to the strongest user or the idx₁ ^(th) user providing better sumcapacity than using spatial multiplexing. The preceding matrix F may bechosen using the following expression:F=v* _(idx) ₁   (17)Control then passes to end step 224.

FIG. 2B is a flow chart that illustrates exemplary steps in a method fora range reduction scheme for user selection in a multiuser MIMO downlinktransmission, in accordance with an embodiment of the invention.Referring to FIG. 2B, in step 252, channel state information (CSI) maybe derived, step 254 comprises a search for optimal users, step 256 maycomprise computing a cumulative distribution function (CDF) among userindexes, and step 258 may comprise computing a reduced search range.

In step 252 CSI may be derived based on a plurality of T independentchannel realizations, for example:{h _(k)(t)}_(k=1) ^(K) ,t=1, . . . , T.  (18)The CSI may comprise channel gain, or signal gain, information. For eachchannel realization, users among the full set of K users may be sorted,and indexed, in an order based on the values of the channel gainscorresponding to each of the K users. For example, a user with a largervalue of corresponding channel gain may be placed in the sorted list ata higher index than a user with a smaller value of corresponding channelgain as in the following expression:

$\begin{matrix}{{{{\gamma_{1}(t)} \geq {\gamma_{2}(t)} \geq \ldots \geq {\gamma_{K}(t)}},{t = 1},\ldots\mspace{11mu},T,{where}}{{\gamma_{K}(t)}\overset{\Delta}{=}{{{h_{K}(t)}}^{2}.}}} & (19)\end{matrix}$

The channel measurement may be carried out either by offline channelsounding or by online channel estimation. In a time division duplex(TDD) system, the base station may compute a channel estimate associatedwith an uplink channel, and use the uplink channel estimation as anapproximation of channel estimates for the corresponding downlinkchannel based on a channel reciprocity property between the uplink anddownlink channels.

In step 254, for each of the channel realizations according to (18), theoptimal user group may be determined according to (8) and (10) as in thefollowing expression:

$\begin{matrix}{{{D_{opt}(t)} = {\arg{\max\limits_{{D \subseteq {\{{1,\mspace{11mu}\ldots\mspace{11mu},K}\}}},{{D} \leq M}}{C_{D}(t)}}}},{t = 1},\ldots\mspace{11mu},T,{where}} & (20) \\{{{C_{D}(t)} = {\sum\limits_{i \in D}\;{\log\left( {1 + {{\xi_{i}(t)}{P_{i}(t)}}} \right)}}},} & (21)\end{matrix}$and where ξ_(i)(t) and P_(i)(t) may be as defined in (6) and (9),respectively. D_(opt)(t) may be represented as a row vector thatcontains indexes corresponding to the users contained in the optimalgroup for channel realization t. By representing the index of theoptimal users as a random variable X, the vector as in the followingexpression:

$\begin{matrix}{D_{opt}\overset{\Delta}{=}\left\lbrack {{D_{opt}(1)},{D_{opt}(2)},\ldots\mspace{11mu},{D_{opt}(T)}} \right\rbrack} & (22)\end{matrix}$may contain samples of the random variable X.

In step 256, an estimate of the cumulative distribution function (CDF){circumflex over (F)}(X) of X may be produced based on samples from theoptimal user index vector, X, that was determined in step 254 accordingto (22).

In step 258, a threshold, ∂_(th)ε(0,1], may be selected. The reducedsearch range may then be determined by the relationship as in thefollowing expression:L={circumflex over (F)} ⁻¹(∂_(th))  (23)where {circumflex over (F)}⁻¹(.) is the inverse function of {circumflexover (F)}(.), for example:X={circumflex over (F)} ⁻¹({circumflex over (F)}(X))The threshold may be a measure of the likelihood that a channelrealization, evaluated among the full range of K users, will comprisethe subset of L users.

In various embodiments of the invention, expression (23) may beimplemented by tabulating the CDF {circumflex over (F)}(X) in terms ofthe random variable comprising the index of optimal users X, andsearching for a value of X that corresponds to ∂_(th). The threshold∂_(th) may provide a measure of the statistical likelihood that the sumrate, computed among of subset of L users in the reduced searchingrange, may approach the optimal performance computed among the fullgroup of K users.

While the exemplary embodiment of the invention illustrates a searchrange reduction scheme a system that utilizes a simple zero-forcingprecoder, the invention is not so limited. Various embodiments of theinvention may also be utilized with other more sophisticated precoders,for example a minimum mean squared error (MMSE) precoder, aTomlinson-Harashima preceding (THP) precoder, or a sphere encodingprecoder, for example.

FIG. 3 is a graph 302 illustrating a comparison of sum capacity of asystem using an optimal brute force user selection with L=100, a doublesearch algorithm with L=10, and a double search algorithm with L=5, inaccordance with an embodiment of the invention. Referring to FIG. 3,there is shown a waveform 304 representing sum capacity of a systemusing an optimal brute force user selection with L=100, a waveform 306representing sum capacity of a system using a double search algorithmwith L=10, and a waveform 308 representing sum capacity of a systemusing a double search algorithm with L=5.

Referring to FIG. 3, the graph 302 illustrates comparison of sumcapacity of a system for a single base station and K=100 users, forexample. The base station, for example, base station 170 may be equippedwith M=2 antennas, and each user may be equipped with a single antenna.The channels are generated to be flat Rayleigh faded. The transmitantennas at the base station 170 may be assumed to be placed apartenough so as to experience independent fading. The modulation format maybe quadrature phase-shift keying (QPSK).

TABLE 1 illustrates a comparison of user selection schemes and thecorresponding search complexity for M=2, K=100.

TABLE 1 Selection schemes Brute force Double search Double search with L= 100 with L = 10 with L = 5 The number of 5050 17 7 candidate usergroupsReferring to TABLE 1, there is shown that the number of candidate usergroups may be

$\frac{L\left( {L + 1} \right)}{2}$for the brute-forcing search algorithm while the double search algorithmmay have only 2 L−3 candidate user groups.

FIG. 4 is a graph 402 illustrating a comparison of bit error rate (BER)of a system using an optimal brute force user selection with L=100, adouble search algorithm with L=10 and a double search algorithm withL=5, in accordance with an embodiment of the invention. Referring toFIG. 4, there is shown a waveform 404 representing BER of a system usingan optimal brute force user selection with L=100, a waveform 406representing BER of a system using a double search algorithm with L=10,and a waveform 408 representing BER of a system using a double searchalgorithm with L=5.

Referring to FIG. 4, the graph 402 illustrates comparison of BER of asystem for a single base station and K=100 users, for example. The basestation, for example, base station 170 may be equipped with M=2antennas, and each user may be equipped with a single antenna. Thechannels are generated to be flat Rayleigh faded. The transmit antennasat the base station 170 may be assumed to be spaced or separated apartenough so as to experience independent fading. The modulation format maybe quadrature phase-shift keying (QPSK).

FIG. 3 and FIG. 4 illustrate performance of various user group selectionschemes for zero-forcing preceding in terms of sum capacity and biterror rate (BER), respectively. The double search user group selectionscheme with a reduced search range of L=10 or L=5 may provide close tooptimal performance of the brute-forcing search scheme with a fullsearch range L=100, both in terms of capacity and BER. The brute-forcingsearch algorithm may need to search over

$\frac{L\left( {L + 1} \right)}{2}$user groups for M=2, whereas the double search algorithm may only have 2L−3 candidate user groups. In accordance with an embodiment of theinvention, the user selection technique may be capable of achievingnear-optimal performance while significantly reducing the computationalburden on the base station. Compared to the brute-force user selectionalgorithm, the selection scheme may significantly reduce thecomputational complexity from about K²/2 to 2 L−1 (K>>L), where Krepresents the number of users.

FIG. 5 is a flowchart illustrating double search user group selectionscheme with range reduction in TDD multiuser MIMO downlink transmission,in accordance with an embodiment of the invention. Referring to FIG. 5,there is shown exemplary steps that start at step 502. In step 504, aplurality of signals may be sorted based on a channel gain correspondingto each of the plurality of signals. In step 506, a range reductionalgorithm may be applied to the sorted plurality of users. A desireduser group may be determined by searching among the L strongest usersand may be carried out offline at the system design stage.Notwithstanding, an embodiment of the invention may incorporate anadaptive algorithm to update L at real time with online channelmeasurements while using the offline calculated user range as theinitial value. Based on the reduced search range [1:L], the search maybe restricted within the first L strongest users for a user pair [idx₁,idx₂] that has the maximum instantaneous sum capacity.

In step 508, a first signal for a first user may be selectedcorresponding to a channel gain that is greater than a channel gaincorresponding to a remaining portion of the reduced search range. Instep 510, a first system capacity may be maximized based on the channelgain corresponding to the selected first signal for the first user. Instep 512, a second signal for a second user corresponding to a channelgain from the remaining portion of the reduced search range may beselected that is greater than a channel gain corresponding to aremaining portion of the remaining portion of the reduced search range.In step 514, a second system capacity may be maximized based on thechannel gain corresponding to the selected second signal for the seconduser. In step 516, the system capacity may be maximized based on agreater of the maximized first system capacity and the maximized secondsystem capacity that may be obtained by searching over all the possibleuser groups according to

$\begin{matrix}{C = {\max\limits_{{D \subseteq {\{{1,\mspace{11mu}\ldots\mspace{11mu},K}\}}},{{D} \leq M}}{C_{D}.}}} & (10)\end{matrix}$

In step 518, a user pair index [idx₁, idx₂] corresponding to themaximized system capacity may be determined. If both idx₁ and idx₂ arevalid indices between 1 and K, then the base station 102 a (FIG. 1A) maybe adapted to communicate to both user idx₁ and user idx₂ at the sametime utilizing spatial multiplexing. The transmitter precoding matrix Fmay be formed according to the following expression:F=[v _(idx) ₂ ^(⊥) v _(idx) ₁ ^(⊥)]*/√{square root over (2)},  (15)

where vectors v_(idx) ₁ and v_(idx) ₂ are the unit norm directionalvectors given by the following expression:

$\begin{matrix}{{v_{{idx}_{1}} = \frac{h_{{idx}_{1}}}{h_{{idx}_{1}}}},{v_{{idx}_{2}} = \frac{h_{{idx}_{2}}}{h_{{idx}_{2}}}},} & (16)\end{matrix}$If idx₂ is equal to 0, the base station 102 a may be communicating onlyto the strongest user or the idx^(th) ₁ user providing better sumcapacity than using spatial multiplexing. The preceding matrix F may bechosen using the following expression:F=v* _(idx) ₁   (17)

The double search user group selection scheme with a reduced searchrange of L=10 or L=5 may provide close to optimal performance of thebrute-forcing search scheme with a full search range L=100, both interms of capacity and BER. The brute-forcing search algorithm may needto search over

$\frac{L\left( {L + 1} \right)}{2}$user groups for M=2, whereas the double search algorithm may only have 2L−3 candidate user groups.

In accordance with an embodiment of the invention, a system forprocessing signals in a communication system may comprise circuitry thatmaximizes system capacity based on reducing a search range within whichto find a group of signals having maximum channel gain, for a timedivision duplex (TDD) multiple-input multiple-output (MIMO) system. Fora time division duplex (TDD) system, the base station 102 a (FIG. 1A)may be adapted to estimate the uplink channel and use it as the downlinkchannel based on the channel reciprocity property between the uplink anddownlink. The base station 102 a may be assumed to have channelknowledge of every user through accurate uplink channel estimation.

The system may further comprise circuitry that selects at least one of:a first signal for a first user and a second signal for a second user,which are both within the reduced search range, and which provides amaximum system capacity. The system further comprises circuitry thatselects from the reduced search range, the first signal for the firstuser corresponding to a channel gain that is greater than a channel gaincorresponding to a remaining portion of the reduced search range. Thesystem further comprises circuitry that selects from the remainingportion of the reduced search range, the second signal for the seconduser corresponding to a channel gain that is greater than a channel gaincorresponding to a remaining portion of the remaining portion of thereduced search range.

The system further comprises circuitry that generates the reduced searchrange by sorting a plurality of signals based on a channel gaincorresponding to each of the plurality of signals. A desired user groupmay be determined by searching among the L strongest users and may becarried out offline at the system design stage. Notwithstanding, anembodiment of the invention may incorporate an adaptive algorithm thatupdates L in real time, accordingly, or at a specified time instant,with online channel measurements while using the offline calculated userrange as the initial value. Based on the reduced search range [1:L], thesearch may be restricted within the first L strongest users for a userpair [idx₁, idx₂] that has the maximum instantaneous sum capacity.

The user scheduler 108 a may be adapted to maximize a first systemcapacity based on the channel gain corresponding to the selected firstsignal for the first user. The user scheduler 108 a may be adapted tomaximize a second system capacity based on the channel gaincorresponding to the selected second signal for the second user. Themaximized system capacity is based on a greater of the maximized firstsystem capacity and the maximized second system capacity. A user pairindex [idx₁, idx₂] corresponding to the maximized system capacity may bedetermined.

Another embodiment of the invention may provide a machine-readablestorage, having stored thereon, a computer program having at least onecode section executable by a machine, thereby causing the machine toperform the steps as described above for a double search user groupselection scheme with range reduction in TDD multiuser MIMO downlinktransmission.

Accordingly, the present invention may be realized in hardware,software, or a combination of hardware and software. The presentinvention may be realized in a centralized fashion in at least onecomputer system, or in a distributed fashion where different elementsare spread across several interconnected computer systems. Any kind ofcomputer system or other apparatus adapted for carrying out the methodsdescribed herein is suited. A typical combination of hardware andsoftware may be a general-purpose computer system with a computerprogram that, when being loaded and executed, controls the computersystem such that it carries out the methods described herein.

The present invention may also be embedded in a computer programproduct, which comprises all the features enabling the implementation ofthe methods described herein, and which when loaded in a computer systemis able to carry out these methods. Computer program in the presentcontext means any expression, in any language, code or notation, of aset of instructions intended to cause a system having an informationprocessing capability to perform a particular function either directlyor after either or both of the following: a) conversion to anotherlanguage, code or notation; b) reproduction in a different materialform.

While the present invention has been described with reference to certainembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted withoutdeparting from the scope of the present invention. In addition, manymodifications may be made to adapt a particular situation or material tothe teachings of the present invention without departing from its scope.Therefore, it is intended that the present invention not be limited tothe particular embodiment disclosed, but that the present invention willinclude all embodiments falling within the scope of the appended claims.

1. A method for processing signals in a communication system, the methodcomprising: performing by one or more processors and/or circuits,functions comprising: maximizing system capacity for a time divisionduplex (TDD) multiple-input multiple-output (MIMO) system, based onreducing a search range within which to find a group of signals havingmaximum channel gain.
 2. The method according to claim 1, said functionscomprising selecting one or both of the following: a first signal for afirst user and a second signal for a second user, which are both withinsaid reduced search range, and which provides a maximum system capacity.3. The method according to claim 2, said functions comprising selectingfrom said reduced search range, said first signal for said first usercorresponding to a channel gain that is greater than a channel gaincorresponding to a remaining portion of said reduced search range. 4.The method according to claim 2, said functions comprising selectingfrom said remaining portion of said reduced search range, said secondsignal for said second user corresponding to a channel gain that isgreater than a channel gain corresponding to a remaining portion of saidremaining portion of said reduced search range.
 5. The method accordingto claim 2, said functions comprising generating said reduced searchrange by sorting a plurality of signals based on a channel gaincorresponding to each of said plurality of signals.
 6. The methodaccording to claim 2, said functions comprising maximizing a firstsystem capacity based on said channel gain corresponding to saidselected first signal for said first user.
 7. The method according toclaim 6, said functions comprising maximizing a second system capacitybased on said channel gain corresponding to said selected second signalfor said second user.
 8. The method according to claim 7, wherein saidmaximized system capacity is based on a greater of said maximized firstsystem capacity and said maximized second system capacity.
 9. Anon-transitory computer-readable storage medium having stored thereon, acomputer program having at least one code section for processing signalsin a communication system, the at least one code section beingexecutable by a computer system for causing the computer system toperform steps comprising: maximizing system capacity for a time divisionduplex (TDD) multiple-input multiple-output (MIMO) system, based onreducing a search range within which to find a group of signals havingmaximum channel gain.
 10. The non-transitory computer-readable storagemedium according to claim 9, wherein said at least one code sectioncomprises code for selecting one or both of the following: a firstsignal for a first user and a second signal for a second user, which areboth within said reduced search range, and which provides a maximumsystem capacity.
 11. The non-transitory computer-readable storage mediumaccording to claim 10, wherein said at least one code section comprisescode for selecting from said reduced search range, said first signal forsaid first user corresponding to a channel gain that is greater than achannel gain corresponding to a remaining portion of said reduced searchrange.
 12. The non-transitory computer-readable storage medium accordingto claim 10, wherein said at least one code section comprises code forselecting from said remaining portion of said reduced search range, saidsecond signal for said second user corresponding to a channel gain thatis greater than a channel gain corresponding to a remaining portion ofsaid remaining portion of said reduced search range.
 13. Thenon-transitory computer-readable storage medium according to claim 10,wherein said at least one code section comprises code for generatingsaid reduced search range by sorting a plurality of signals based on achannel gain corresponding to each of said plurality of signals.
 14. Thenon-transitory computer-readable storage medium according to claim 10,wherein said at least one code section comprises code for maximizing afirst system capacity based on said channel gain corresponding to saidselected first signal for said first user.
 15. The non-transitorycomputer-readable storage medium according to claim 14, wherein said atleast one code section comprises code for maximizing a second systemcapacity based on said channel gain corresponding to said selectedsecond signal for said second user.
 16. The non-transitorycomputer-readable storage medium according to claim 15, wherein saidmaximized system capacity is based on a greater of said maximized firstsystem capacity and said maximized second system capacity.
 17. A systemfor processing signals in a communication system, the system comprising:one or more circuits that enables maximization of system capacity for atime division duplex (TDD) multiple-input multiple-output (MIMO) system,based on reducing a search range within which to find a group of signalshaving maximum channel gain.
 18. The system according to claim 17,wherein said one or more circuits enables selection of one or both ofthe following: a first signal for a first user and a second signal for asecond user, which are both within said reduced search range, and whichprovides a maximum system capacity.
 19. The system according to claim18, wherein said one or more circuits enables selection from saidreduced search range, said first signal for said first usercorresponding to a channel gain that is greater than a channel gaincorresponding to a remaining portion of said reduced search range. 20.The system according to claim 18, wherein said one or more circuitsenables selection from said remaining portion of said reduced searchrange, said second signal for said second user corresponding to achannel gain that is greater than a channel gain corresponding to aremaining portion of said remaining portion of said reduced searchrange.
 21. The system according to claim 18, wherein said one or morecircuits enables generation of said reduced search range by sorting aplurality of signals based on a channel gain corresponding to each ofsaid plurality of signals.
 22. The system according to claim 18, whereinsaid one or more circuits enables maximization of a first systemcapacity based on said channel gain corresponding to said selected firstsignal for said first user.
 23. The system according to claim 22,wherein said one or more circuits enables maximization of a secondsystem capacity based on said channel gain corresponding to saidselected second signal for said second user.
 24. The system according toclaim 23, wherein said maximized system capacity is based on a greaterof said maximized first system capacity and said maximized second systemcapacity.